Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory

1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720-1770, USA 2Flarion Technologies Inc., Bedminster, NJ 07921, USA 3Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA 4Department of Electrical Engineering and Computer Sciences...

متن کامل

Estimation error bounds for denoising by sparse approximation

If a signal is known to have a sparse representation with respect to a given frame, thc signal can he estimated from a noise-corrupted observation of the signal by finding the hest sparse approximation to the observation. ‘The ability to remove noise in this manner depends on the frame heing designed to efficiently represent the signal while it ine@cienf/v represents the noise. ‘This paper give...

متن کامل

Estimation via Sparse Approximation : Error Bounds and Random Frame Analysis

Estimation via Sparse Approximation: Error Bounds and Random Frame Analysis

متن کامل

Rate-Distortion Bounds for Kernel-Based Distortion Measures

Kernel methods have been used for turning linear learning algorithms into nonlinear ones. These nonlinear algorithms measure distances between data points by the distance in the kernel-induced feature space. In lossy data compression, the optimal tradeoff between the number of quantized points and the incurred distortion is characterized by the rate-distortion function. However, the rate-distor...

متن کامل

A sparse-response deep belief network based on rate distortion theory

Deep belief networks (DBNs) are currently the dominant technique for modeling the architectural depth of brain, and can be trained efficiently in a greedy layer-wise unsupervised learning manner. However, DBNs without a narrow hidden bottleneck typically produce redundant, continuous-valued codes and unstructured weight patterns. Taking inspiration from rate distortion (RD) theory, which encode...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: EURASIP Journal on Advances in Signal Processing

سال: 2006

ISSN: 1687-6172,1687-6180

DOI: 10.1155/asp/2006/26318